Can Synthetic UAV Benchmarks Be Trusted? Real-Synthetic Validation for UAV Model Selection

Title:Can Synthetic UAV Benchmarks Be Trusted? Real-Synthetic Validation for UAV Model Selection

Abstract:Synthetic UAV imagery can expand pre-deployment test evidence when task-specific Real data are scarce, but visual plausibility or semantic label alignment alone does not establish validity for model selection. We study when Synthetic evaluation preserves the model-selection conclusions obtained from Real UAV data through two complementary cases under fixed evaluation protocols. In a requirement-specific road-debris benchmark, four language-conditioned perception systems obtain lower absolute F1 on Synthetic data, yet the point-estimate ordering and top-ranked model are preserved; bootstrap analysis yields no supported pairwise reversal, and size-matched resampling preserves the top-model decision. In contrast, a closed-set stress test on a separately materialized business corpus, using four VisDrone-compatible detectors over eight empirically supported classes, changes the point-estimate top-ranked detector and reverses three of six pairwise point-estimate preference directions. A complementary audit of five UAV business scenarios further reveals substantial Real-Synthetic mismatch in class composition, object density, and target scale, including systematic under-representation of small targets. These results show that Synthetic UAV benchmark trust is conditional: semantic coverage is insufficient, and validity must be established empirically for the task, operating regime, and model panel of interest.

Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)

arXiv:2610.14647 [cs.CV]

(or arXiv:2610.14647v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2610.14647 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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